{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-10-24T14:07:54.012620600Z",
     "start_time": "2023-10-24T14:07:53.986718400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/dear_breaker/week7深度学习之多层感知机\n",
      "['data.csv', '.ipynb_checkpoints']\n"
     ]
    },
    {
     "data": {
      "text/plain": "       x1      x2  y\n0  0.0323  0.0244  1\n1  0.0887  0.0244  1\n2  0.1690  0.0163  1\n3  0.2420  0.0000  1\n4  0.2420  0.0488  1",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>x1</th>\n      <th>x2</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0323</td>\n      <td>0.0244</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0887</td>\n      <td>0.0244</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.1690</td>\n      <td>0.0163</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.2420</td>\n      <td>0.0000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.2420</td>\n      <td>0.0488</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "print(os.getcwd())\n",
    "print(os.listdir(os.getcwd()))\n",
    "data = pd.read_csv('data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "       x1      x2\n0  0.0323  0.0244\n1  0.0887  0.0244\n2  0.1690  0.0163\n3  0.2420  0.0000\n4  0.2420  0.0488",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>x1</th>\n      <th>x2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0323</td>\n      <td>0.0244</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0887</td>\n      <td>0.0244</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.1690</td>\n      <td>0.0163</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.2420</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.2420</td>\n      <td>0.0488</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.drop(['y'], axis=1)\n",
    "x.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-24T14:07:54.018620600Z",
     "start_time": "2023-10-24T14:07:53.998621500Z"
    }
   },
   "id": "509accb5a39ae6f"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "0    1\n1    1\n2    1\n3    1\n4    1\nName: y, dtype: int64"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data.loc[:, 'y']\n",
    "y.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-24T14:07:54.150148600Z",
     "start_time": "2023-10-24T14:07:54.003631500Z"
    }
   },
   "id": "c6bbed9bd4d4fe6b"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "passed = plt.scatter(x.loc[:, 'x1'][y == 1], x.loc[:, 'x2'][y == 1])\n",
    "\n",
    "fialed = plt.scatter(x.loc[:, 'x1'][y == 0], x.loc[:, 'x2'][y == 0])\n",
    "\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.title('raw data')\n",
    "plt.legend((passed, fialed), ('passed', 'failed'))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-24T14:07:54.300225100Z",
     "start_time": "2023-10-24T14:07:54.049217Z"
    }
   },
   "id": "83332367b39f3a7f"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(275, 2) (136, 2)\n"
     ]
    }
   ],
   "source": [
    " # 分离数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=10)\n",
    "print(x_train.shape, x_test.shape)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-24T14:19:05.595375600Z",
     "start_time": "2023-10-24T14:19:05.583438900Z"
    }
   },
   "id": "1ba8ce085332bd18"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_2 (Dense)             (None, 20)                60        \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 21        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 81\n",
      "Trainable params: 81\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "mlp = Sequential()\n",
    "\n",
    "mlp.add(Dense(units=20, input_dim=2, activation='sigmoid'))\n",
    "mlp.add(Dense(units=1, activation='sigmoid'))\n",
    "mlp.summary()\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-24T14:23:28.859818700Z",
     "start_time": "2023-10-24T14:23:28.793815800Z"
    }
   },
   "id": "c9c5ae07ad699b8e"
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "mlp.compile(optimizer='sgd', loss='binary_crossentropy')\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-25T04:48:48.192538700Z",
     "start_time": "2023-10-25T04:48:48.187538800Z"
    }
   },
   "id": "76f97efe8cc1a3d7"
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5000\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.0920\n",
      "Epoch 2/5000\n",
      "9/9 [==============================] - 0s 791us/step - loss: 0.0920\n",
      "Epoch 3/5000\n",
      "9/9 [==============================] - 0s 701us/step - loss: 0.0920\n",
      "Epoch 4/5000\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.0920\n",
      "Epoch 5/5000\n",
      "9/9 [==============================] - 0s 724us/step - loss: 0.0920\n",
      "Epoch 6/5000\n",
      "9/9 [==============================] - 0s 678us/step - loss: 0.0920\n",
      "Epoch 7/5000\n",
      "9/9 [==============================] - 0s 618us/step - loss: 0.0920\n",
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      "Epoch 4828/5000\n",
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      "Epoch 4830/5000\n",
      "9/9 [==============================] - 0s 661us/step - loss: 0.0741\n",
      "Epoch 4831/5000\n",
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      "Epoch 4832/5000\n",
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      "Epoch 4833/5000\n",
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      "Epoch 4838/5000\n",
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      "Epoch 4839/5000\n",
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      "Epoch 4841/5000\n",
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      "Epoch 4842/5000\n",
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      "Epoch 4843/5000\n",
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      "Epoch 4851/5000\n",
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      "Epoch 4854/5000\n",
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      "Epoch 4855/5000\n",
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      "Epoch 4856/5000\n",
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      "Epoch 4857/5000\n",
      "9/9 [==============================] - 0s 736us/step - loss: 0.0741\n",
      "Epoch 4858/5000\n",
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      "Epoch 4859/5000\n",
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      "Epoch 4860/5000\n",
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      "Epoch 4861/5000\n",
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      "Epoch 4862/5000\n",
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      "Epoch 4863/5000\n",
      "9/9 [==============================] - 0s 729us/step - loss: 0.0740\n",
      "Epoch 4864/5000\n",
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      "Epoch 4870/5000\n",
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      "Epoch 4871/5000\n",
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      "Epoch 4872/5000\n",
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      "Epoch 4873/5000\n",
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      "Epoch 4875/5000\n",
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      "Epoch 4876/5000\n",
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      "Epoch 4877/5000\n",
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      "Epoch 4878/5000\n",
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      "Epoch 4881/5000\n",
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      "Epoch 4882/5000\n",
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      "Epoch 4884/5000\n",
      "9/9 [==============================] - 0s 704us/step - loss: 0.0740\n",
      "Epoch 4885/5000\n",
      "9/9 [==============================] - 0s 692us/step - loss: 0.0740\n",
      "Epoch 4886/5000\n",
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      "Epoch 4887/5000\n",
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      "Epoch 4888/5000\n",
      "9/9 [==============================] - 0s 697us/step - loss: 0.0740\n",
      "Epoch 4889/5000\n",
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      "Epoch 4890/5000\n",
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      "Epoch 4891/5000\n",
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      "Epoch 4892/5000\n",
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      "Epoch 4893/5000\n",
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      "Epoch 4894/5000\n",
      "9/9 [==============================] - 0s 750us/step - loss: 0.0740\n",
      "Epoch 4895/5000\n",
      "9/9 [==============================] - 0s 672us/step - loss: 0.0740\n",
      "Epoch 4896/5000\n",
      "9/9 [==============================] - 0s 713us/step - loss: 0.0739\n",
      "Epoch 4897/5000\n",
      "9/9 [==============================] - 0s 827us/step - loss: 0.0739\n",
      "Epoch 4898/5000\n",
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      "Epoch 4900/5000\n",
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      "Epoch 4901/5000\n",
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      "Epoch 4903/5000\n",
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      "Epoch 4904/5000\n",
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      "Epoch 4906/5000\n",
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      "Epoch 4907/5000\n",
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      "Epoch 4908/5000\n",
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      "Epoch 4909/5000\n",
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      "Epoch 4910/5000\n",
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      "Epoch 4912/5000\n",
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      "Epoch 4913/5000\n",
      "9/9 [==============================] - 0s 862us/step - loss: 0.0739\n",
      "Epoch 4914/5000\n",
      "9/9 [==============================] - 0s 792us/step - loss: 0.0739\n",
      "Epoch 4915/5000\n",
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      "Epoch 4916/5000\n",
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      "Epoch 4917/5000\n",
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      "Epoch 4918/5000\n",
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      "Epoch 4919/5000\n",
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      "Epoch 4920/5000\n",
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      "Epoch 4921/5000\n",
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      "Epoch 4922/5000\n",
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      "Epoch 4923/5000\n",
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      "Epoch 4928/5000\n",
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      "Epoch 4931/5000\n",
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      "Epoch 4932/5000\n",
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      "Epoch 4933/5000\n",
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      "Epoch 4934/5000\n",
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      "Epoch 4935/5000\n",
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      "Epoch 4936/5000\n",
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      "Epoch 4937/5000\n",
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      "Epoch 4938/5000\n",
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      "Epoch 4939/5000\n",
      "9/9 [==============================] - 0s 772us/step - loss: 0.0739\n",
      "Epoch 4940/5000\n",
      "9/9 [==============================] - 0s 810us/step - loss: 0.0738\n",
      "Epoch 4941/5000\n",
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      "Epoch 4942/5000\n",
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      "Epoch 4943/5000\n",
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      "Epoch 4944/5000\n",
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      "Epoch 4946/5000\n",
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      "Epoch 4947/5000\n",
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      "Epoch 4948/5000\n",
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      "Epoch 4949/5000\n",
      "9/9 [==============================] - 0s 791us/step - loss: 0.0738\n",
      "Epoch 4950/5000\n",
      "9/9 [==============================] - 0s 665us/step - loss: 0.0738\n",
      "Epoch 4951/5000\n",
      "9/9 [==============================] - 0s 750us/step - loss: 0.0738\n",
      "Epoch 4952/5000\n",
      "9/9 [==============================] - 0s 666us/step - loss: 0.0738\n",
      "Epoch 4953/5000\n",
      "9/9 [==============================] - 0s 675us/step - loss: 0.0738\n",
      "Epoch 4954/5000\n",
      "9/9 [==============================] - 0s 756us/step - loss: 0.0738\n",
      "Epoch 4955/5000\n",
      "9/9 [==============================] - 0s 773us/step - loss: 0.0738\n",
      "Epoch 4956/5000\n",
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      "Epoch 4957/5000\n",
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      "Epoch 4958/5000\n",
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      "Epoch 4959/5000\n",
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      "Epoch 4960/5000\n",
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      "Epoch 4961/5000\n",
      "9/9 [==============================] - 0s 697us/step - loss: 0.0738\n",
      "Epoch 4962/5000\n",
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      "Epoch 4963/5000\n",
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      "Epoch 4964/5000\n",
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      "Epoch 4965/5000\n",
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      "Epoch 4966/5000\n",
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      "Epoch 4967/5000\n",
      "9/9 [==============================] - 0s 700us/step - loss: 0.0738\n",
      "Epoch 4968/5000\n",
      "9/9 [==============================] - 0s 800us/step - loss: 0.0738\n",
      "Epoch 4969/5000\n",
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      "Epoch 4970/5000\n",
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      "Epoch 4971/5000\n",
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      "Epoch 4972/5000\n",
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      "Epoch 4973/5000\n",
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      "Epoch 4974/5000\n",
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      "Epoch 4975/5000\n",
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      "Epoch 4976/5000\n",
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      "Epoch 4977/5000\n",
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      "Epoch 4978/5000\n",
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      "Epoch 4979/5000\n",
      "9/9 [==============================] - 0s 764us/step - loss: 0.0737\n",
      "Epoch 4980/5000\n",
      "9/9 [==============================] - 0s 718us/step - loss: 0.0737\n",
      "Epoch 4981/5000\n",
      "9/9 [==============================] - 0s 689us/step - loss: 0.0737\n",
      "Epoch 4982/5000\n",
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      "Epoch 4983/5000\n",
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      "Epoch 4984/5000\n",
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      "Epoch 4985/5000\n",
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      "Epoch 4986/5000\n",
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      "Epoch 4987/5000\n",
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      "Epoch 4988/5000\n",
      "9/9 [==============================] - 0s 739us/step - loss: 0.0737\n",
      "Epoch 4989/5000\n",
      "9/9 [==============================] - 0s 736us/step - loss: 0.0737\n",
      "Epoch 4990/5000\n",
      "9/9 [==============================] - 0s 824us/step - loss: 0.0737\n",
      "Epoch 4991/5000\n",
      "9/9 [==============================] - 0s 757us/step - loss: 0.0737\n",
      "Epoch 4992/5000\n",
      "9/9 [==============================] - 0s 759us/step - loss: 0.0737\n",
      "Epoch 4993/5000\n",
      "9/9 [==============================] - 0s 767us/step - loss: 0.0737\n",
      "Epoch 4994/5000\n",
      "9/9 [==============================] - 0s 764us/step - loss: 0.0737\n",
      "Epoch 4995/5000\n",
      "9/9 [==============================] - 0s 727us/step - loss: 0.0737\n",
      "Epoch 4996/5000\n",
      "9/9 [==============================] - 0s 682us/step - loss: 0.0737\n",
      "Epoch 4997/5000\n",
      "9/9 [==============================] - 0s 694us/step - loss: 0.0737\n",
      "Epoch 4998/5000\n",
      "9/9 [==============================] - 0s 639us/step - loss: 0.0737\n",
      "Epoch 4999/5000\n",
      "9/9 [==============================] - 0s 688us/step - loss: 0.0737\n",
      "Epoch 5000/5000\n",
      "9/9 [==============================] - 0s 790us/step - loss: 0.0737\n"
     ]
    },
    {
     "data": {
      "text/plain": "<keras.callbacks.History at 0x7fd07c4bf100>"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.fit(x=x_train, y=y_train, epochs=5000)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-25T04:53:39.077114300Z",
     "start_time": "2023-10-25T04:52:58.050838400Z"
    }
   },
   "id": "b05d5cf786a900e6"
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 790us/step\n",
      "0.509090909090909\n"
     ]
    }
   ],
   "source": [
    "# 版本太高了，下面predict_classes这个用不了，已经被弃用了\n",
    "# y_train_prediction = mlp.predict_classes(x_train)\n",
    "y_train_prediction = mlp.predict(x_train)\n",
    "# print(y_train_prediction)\n",
    "y_train_prediction_class = np.argmax(y_train_prediction, axis=1)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_rate = accuracy_score(y_train, y_train_prediction_class)\n",
    "print(accuracy_rate)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-25T05:40:07.168234Z",
     "start_time": "2023-10-25T05:40:07.122234600Z"
    }
   },
   "id": "b902bb606febbef8"
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 830us/step\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "0.4632352941176471\n"
     ]
    }
   ],
   "source": [
    " y_test_prindiction = mlp.predict(x_test)\n",
    "y_test_prindiction_classes = np.argmax(y_test_prindiction, axis=1)\n",
    "print(y_test_prindiction_classes)\n",
    "print(accuracy_score(y_test, y_test_prindiction_classes))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-25T04:44:35.026298500Z",
     "start_time": "2023-10-25T04:44:34.959293200Z"
    }
   },
   "id": "2b05d08cf12fe075"
  }
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